code: def get_properties(dataset_properties=None):
      return {
          "shortname": "RF",
          "name": "Random Forest Classifier",
          "handles_regression": False,
          "handles_classification": True,
          "handles_multiclass": True,
          "handles_multilabel": True,
          "handles_multioutput": False,
          "is_deterministic": True,
          "input": (DENSE, SPARSE, UNSIGNED_DATA),
          "output": (PREDICTIONS,),
      }
  
file_path: auto-sklearn/examples/80_extending/example_restrict_number_of_hyperparameters.py
imports: from typing import Optional
  from ConfigSpace.configuration_space import ConfigurationSpace
  from ConfigSpace.hyperparameters import UniformIntegerHyperparameter, UniformFloatHyperparameter
  from sklearn.datasets import load_breast_cancer
  from sklearn.model_selection import train_test_split
  from autosklearn.askl_typing import FEAT_TYPE_TYPE
  import autosklearn.classification
  import autosklearn.pipeline.components.classification
  from autosklearn.pipeline.components.classification import AutoSklearnClassificationAlgorithm
  from autosklearn.pipeline.constants import DENSE, UNSIGNED_DATA, PREDICTIONS, SPARSE
label_desc: Use image node_id 5 for calling the CustomRandomForest obj's underlying member method code with example usage: obj.get_properties(dataset_properties) and returns: dict